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FAULT IDENTIFICATION USING FINITE ELEMENT MODELS AND NEURAL NETWORKS

✍ Scribed by T. MARWALA; H.E.M. HUNT

Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
339 KB
Volume
13
Category
Article
ISSN
0888-3270

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✦ Synopsis

When vibration data are used to identify faults in structures it is not completely clear whether to use either frequency response functions or modal parameters. This paper presents a committee of neural networks technique, which employs both frequency response functions and modal data simultaneously to identify faults in structures. The new approach is tested on simulated data from a cantilevered beam, which is substructured into "ve regions. It is observed that irrespective of the noise levels in the data, the committee of neural networks gives results that have lower mean-squares errors and standard deviations than the two existing methods. It is found that the new method is able to identify fault cases better than the two approaches used individually. It is established that for the problem analysed, giving equal weights to the frequency-response-based method and modal-properties-based method minimise the errors on identifying faults.

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